Curriculum Overview: Data Quality and Validation

This curriculum focuses on the principles and practical implementation of data quality frameworks within the AWS ecosystem, specifically targeting the AWS Certified Data Engineer - Associate (DEA-C01). Students will learn to transition from reactive troubleshooting to proactive, automated data validation using tools like AWS Glue Data Quality and AWS Glue DataBrew.

Prerequisites

Before starting this module, students should possess the following foundational knowledge:

• Cloud Fundamentals: Basic understanding of Amazon S3 storage and AWS IAM permissions.
• Data Processing: Familiarity with ETL (Extract, Transform, Load) concepts and Apache Spark basics.
• Querying: Proficiency in SQL for data inspection and verification.
• Infrastructure: Understanding of AWS Glue Crawlers and the Data Catalog.

Module Breakdown

Learning Objectives per Module

DQV-01: The Deequ Framework & DQDL

• Explain the role of the open-source Deequ library in AWS services.
• Write declarative rules using DQDL (Data Quality Definition Language).
• Differentiate between the four dimensions of quality: Consistency, Completeness, Accuracy, and Integrity.

DQV-02: AWS Glue Data Quality

• Generate data quality rule recommendations from existing AWS Glue Data Catalog tables.
• Integrate data quality checks directly into Spark-based ETL scripts.
• Inspect computed metrics using the VerificationResult API.

DQV-03: Visual Profiling & Cleansing

• Perform data profiling to identify missing values, data types, and range anomalies.
• Apply over 250 built-in transformations in DataBrew without writing code.
• Implement data sampling techniques for large-scale datasets.

DQV-04: Automation & Error Handling

• Configure Amazon EventBridge to trigger Lambda functions upon DQ failure.
• Implement "Dead Letter Queues" (DLQ) for records that fail validation.
• Set up automated retries for transient connection errors using AWS Step Functions.

Visual Anchors

Data Quality Workflow

The Hierarchy of Data Profiling

Success Metrics

To demonstrate mastery of this curriculum, students must be able to:

1. Define Assertions: Successfully write a DQDL script that validates that a "Month" column is between 1 and 12 and that "Email" is never NULL.
2. Calculate Quality Scores: Implement a metric where the Success Rate ($S$) is calculated as: $S = \frac{\text{Passed Rules}}{\text{Total Rules}} \times 100$
3. Automate Remediation: Build a workflow where data with a quality score $< 90\%$ is automatically routed to an S3 error prefix.

[!IMPORTANT] AWS Glue Data Quality is serverless. This means you do not need to manage Spark clusters manually to run quality checks; the service scales automatically with your data volume.

Real-World Application

Case Study: Healthcare Accuracy

In a study published in PubMed, researchers found that incorrect recording of patient weights (even at a low error rate of 0.63%) led to medication-dosing errors in 34% of those cases.

Application: Using the tools in this curriculum, a Data Engineer would:

• Rule: Use DQDL to ensure patient_weight is within a biological range (e.g., $2kg < x < 500kg$).
• Validation: Use DataBrew to profile the dosage column against the weight column to find statistical outliers.
• Impact: Automated validation prevents the data from reaching the downstream clinical application, potentially saving lives by ensuring medication safety.

▶Click to view a sample DQDL Rule Set

sqlCopy

Rules = [ IsComplete "patient_id", ColumnValues "patient_weight" between 2 and 500, ColumnDataType "visit_date" = "Date", RowCount > 0 ]